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A Perceptually Motivated Approach for Speech Enhancement Based on Deep Neural Network
Wei HAN Xiongwei ZHANG Gang MIN Meng SUN
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2016/04/01
Online ISSN: 1745-1337
Type of Manuscript: LETTER
Category: Speech and Hearing
perceptually motivated, deep neural network, speech enhancement, masking residual noise,
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In this letter, a novel perceptually motivated single channel speech enhancement approach based on Deep Neural Network (DNN) is presented. Taking into account the good masking properties of the human auditory system, a new DNN architecture is proposed to reduce the perceptual effect of the residual noise. This new DNN architecture is directly trained to learn a gain function which is used to estimate the power spectrum of clean speech and shape the spectrum of the residual noise at the same time. Experimental results demonstrate that the proposed perceptually motivated speech enhancement approach could achieve better objective speech quality when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.